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Thales’s Big analytics approach extracts every ounce of information from your data to better understand passenger behaviour and ridership patterns and know how they really use your transport infrastructure and equipment.

Public transport authorities and operators gather enormous amounts of data, much of it generated by smart travel cards. Yet this data is only used to a fraction of its true potential. Every day, Thales systems process 50 million transactions in more than 100 cities. In all, our ticketing systems process transactions worth 20 billion dollars around the globe every year.

Our Big analytics approach helps you to optimise your transportation networks by extracting every ounce of information from this data so you can better understand how passengers use your systems. We reason in terms of overall flows and ridership patterns, so data can be processed anonymously to protect passenger privacy.

The very concept of a smart city is based on making intelligent use of data generated by city-dwellers to help optimise services. Thales's value proposition is unique because we combine an in-depth knowledge of all the processes involved in managing a smart city with the technology leadership required to analyse vast amounts of data cost-effectively.

Smart cities promise an El Dorado for Big data

The exponential growth of connected objects offers cities new opportunities to attract business and improve the quality of life of their citizens. Thales’s Big data technologies make sense of vast volumes of data and help them make better strategic decisions.

Competition between cities is hitting up in today’s globalised world. Climate change and growing pressure on natural resources make it more important than ever to produce and consume responsibly. As a result, cities are revisiting their strategic priorities and planning for a model of development that is more sustainable, smarter and more creative.

Cities are a nexus of resources, skills and stakeholders — and fertile ground for innovation and the development of Big data applications.

The impending dataclysm

Smartphones, public lighting, transport systems, billboards and displays, access control systems at public places and CCTV cameras are just some of the ever-increasing number of connected urban objects managed by different types of information systems. According to some projections[1], there will be 50 billion connected objects by 2020. With a world population of 7.6 billion, that’s seven objects per person, compared to just 0.03 in 2003. And 90% of all data available in the world has been generated within the last two years.

The ability to store, process, comprehend and exploit this torrent of bits and bytes will allow urban policymakers and stakeholders to work more closely together to understand and anticipate the needs of city dwellers and respond to their expectations.

Collection, processing and exploitation

Thales’s expertise covers the full spectrum of Big data technologies.

It all starts with data collection, storage and retrieval. Thales brings to the table a unique set of skills and resources, designing and implementing database systems that can handle vast volumes of heterogeneous data fast enough to make the data useful. Thales also has the expertise in IT security needed to guarantee the integrity and value of the data — in other words to make sure the right datasets have been collected and have not been corrupted in the process (for more about this subject, see The Four Vs of Big Data).

Next comes dataanalysis. Thales’s Big analytics data scientists are developing algorithms capable of making sense of very large volumes of data to inform strategic decisions. For cities, information about security situations can be crucial. For example, Big analytics technologies are able to sift through the data arriving from a whole range of platforms — CCTV cameras, ticketing systems, call centres, etc. — to detect unsuspected hot spots and optimise deployment of police and security services.

Anticipating citizens’ expectations

Big data techniques are also a great way to predict future patterns and anticipate risky situations. Visual analytics technologies, for example, are the basis for the smart visualisation solutions developed by Thales. These provide interactive graphs showing the dynamics of change and the impact of events in real time. Decision-makers can adjust different parameters and immediately see how they impact the overall system — in this case the city.

If two lanes of a four-lane highway are closed, for example, graphs show the resulting tailbacks and effects on traffic on other roads in the area. Likewise, taking a bus out of service will lead to more people waiting at bus stops, potentially causing security problems, crowding and unhappy passengers. Interactive graphs make it easier to change, understand and anticipate such situations in real time.

By pulling together information from multiple databases — water and energy consumers, subscribers to public and private services, transport users, etc. — city managers have powerful tools at their fingertips to know what citizens want and set up the appropriate channels of communication (websites, mobile apps, etc.). Some of these tools already exist and many more are in the pipeline.

Thales is working with cities around the world to develop these new tools and discover the untold treasures lying in today's huge silos of underused data. Big data is the new El Dorado for smart cities. It's the key to their ability to provide citizens the services they expect — and some they never even dreamed of!

Big analytics and the future of public transport

The intelligent transport systems that Thales designs for its customers generate huge amounts of data. Today, Big data technologies can analyse that information and transform it into valuable insights for fleet and network operators. Toronto has been putting these technologies to the test.

Since the end of 2009, travellers in the Greater Toronto area have been using a state-of-the-art system called Presto, designed and developed by Thales, to ride trains, buses and the metro system with a single contactless ticket valid across the entire network.

About a dozen different public transport operators have adopted Presto. A range of payment options are available, from ticketing machines in stations to online payment services. But the system doesn't only gather data about how people pay for their travel — it also uses a network of fixed and handheld ticket validators to determine mobility patterns across this huge metropolitan area stretching more than 200 km along the shore of Lake Ontario.

Behaviour analysis

Thales has designed a Big data demonstrator to extrapolate typical traveller profiles and network usage patterns from a corpus of 350 million anonymised transactions.

"We have managed to identify families of users who use the metro at the same time every day and who have identical behaviour patterns. We've also pinpointed some more unexpected trends, such as large numbers of students travelling at lunchtime and around 9 pm," says Jean Costantini, senior business advisor at Thales. "Operators can use insights like these to check that the available transport options match passenger demand at any given time, then adjust resources accordingly to offer a better service. And although the system only checks tickets at the start of each journey, we can also determine where passengers exit the system with an accuracy of 92% or better, thanks to algorithms developed by our Big data specialists, who are working closely with Polytechnique Montréal in Canada and the LIP6 computer science lab at Pierre et Marie Curie University in Paris. For operators, this is a real goldmine of information about where travellers are going and how they get there."

The system can also combine these results with the socio-economic data collected when passengers sign up for an account, opening up additional opportunities and a virtually unlimited range of marketing possibilities. "We can distinguish residential districts from business districts, or detect behaviour patterns at individual stations — all of which could be hugely valuable for launching new services or concessions, for example," continues Jean Costantini.

Endless applications

For transport operators, the benefits are clear. Data from ticketing systems allows them to identify which ticket vending machines and validators are used the most and ensure the right organisation is in place to fix any technical problems.

In addition, by combining this ticketing data with information from signalling and supervision systems — another area where Thales is a world market leader — an entire transportation network can be modelled. This makes it possible to predict the consequences of a service disruption caused by maintenance work or bad weather, for example, and plan alternative forms of transport for passengers.

The system also allows operators to study passenger flows on a regional scale so they can see when different types of transport are almost unused and adjust capacities — for example by using smaller buses, changing routes or offering alternative combinations of transport options. "The system detects anything that isn't normal. For example, travel patterns on Mondays may be different from other weekdays, or there may be variations in usage that seem unpredictable but are actually linked to a particular event like a visiting head of state," says Jean Costantini.

These Big data techniques can even provide hard facts about fare dodgers, for example, so that ticket inspectors can be in the right place at the right time instead of relying on anecdotal evidence or intuition.

Analysing social media is another way that Big data techniques could benefit transport operators. This is a major line of research for Thales's Big data specialists, who have been working in this area for the defence community for a number of years. Complaints on social media, or inflammatory comments, can reveal information on a whole range of subjects as well as identifying opinion leaders who could help transport operators enhance their public image and improve their services.

"The only limit is our own imagination," concludes Jean Costantini. "The data is already there. Thales brings to the table a range of expertise and an in-depth understanding of transport operators' business processes. That puts us in a good position to choose the best Big data technologies and the best analytics and visualisation tools to extract as much relevant information as possible and present it in a way that humans can understand!"

Big data limits the impact of flight delays

Airspace is a system in which all aircraft are interdependent. One delayed flight has an impact on all the other flights at a given airport and also has a wider "butterfly" effect at other airports. Using Big data technologies, Thales analyses huge volumes of air traffic data to predict actual flight durations so that airlines and airports can reduce and/or anticipate delays, and make substantial cost savings in the process.

Flight delays can be due to bad weather, congested skies or runways, or even a large flock of birds in the vicinity of an airport. Whatever the cause, they push up costs for airlines — cancellations, passenger compensation, employee overtime, etc. — and also have an impact on the rest of the air transport system.

Combining their expertise in algorithms and Big data techniques with a detailed understanding of air traffic operations, Thales’s data scientists have developed algorithms to correlate and analyse large sets of air traffic and related data which conventional technologies are simply unable to process exhaustively. The objective is to detect and analyse the factors that cause aircraft delays and accurately predict actual flight durations.

The data is collected as part of Europe’s SESAR programme[1], in which Thales is a major player, and various types are analysed:

Aircraft positions over Europe, provided by the ADS-B (Automatic Dependent Surveillance-Broadcast) system

Flight data (aircraft type, route, altitude, etc.) provided by Eurocontrol, the European organisation for air navigation

Etc.

Analysis reveals which factors are affecting flights — typically weather conditions, runway occupancy rates at peak times and average aircraft delays over the previous hour — and shows close spatial and temporal correlations. For example, a delay at Paris Charles de Gaulle airport at a given time will propagate and either get worse (at peak periods) or better (after the peak period) one hour later. This is known as the snowball effect.

Delays also propagate geographically. A late take-off at Paris leads to a delay and disruption at London Heathrow. This is the "butterfly" effect. Organisations such as Eurocontrol in Europe and the Federal Aviation Administration (FAA) in the United States may also shut down an entire system for safety reasons if weather conditions dictate.

Thales's new algorithms use previous datasets to accurately predict how situations will unfold and how actual aircraft flight times will be impacted. For example, previous flight times are weighted by runway occupancy rates at the destination airport to predict what the actual flight time will be.

All the stakeholders in the air transport system, and airlines in particular, stand to benefit from these predictions. By anticipating delays, airlines can better manage the risks for passengers (by switching them to other flights at an earlier stage, for example) and better allocate aircrews, ground personnel and other resources to minimise disruption.

Propagation of delays from one airport to others

[1] The Single European Sky ATM Research (SESAR) programme will provide Europe with high-performance air traffic management systems by 2020 (www.sesarju.eu).

Ticketing by Thales: Big data goes native

Thales recently launched a new line of fare collection solutions called TransCity™.[1] Modular, scalable and built around an open architecture, TransCity is above all a passenger-centric offering with a host of features that make life easier for travellers while generating valuable insights for operators.

Installed in over 100 cities around the globe, Thales fare collection systems process transactions worth about €15 billion every year. Drawing on more than 30 years of experience in this area, Thales combined the best of the best in fare collection solutions and services to develop the new-generation TransCity offering.

Joining the dots

Based on a web-oriented architecture, TransCity is made up of five modules that can be deployed straight away as a complete package or rolled out progressively, depending on the needs of each project and the type of systems already in place. TransCity offers passengers a wider range of fare media and payment options, including contactless smartcards, credit cards, QR codes, smartphones and online payments.

As transport networks become more interconnected, TransCity offers transport authorities and operators a whole range of new opportunities by analysing huge volumes of usage data and generating indicators about ridership patterns. In the Netherlands, for example, where Thales has deployed a nationwide contactless fare collection system, an average of 6 million journeys and almost 12 million transactions are processed every day.

Data on payment methods and ticket validation is collected by the TransCity UP module, which manages, monitors and consolidates financial transactions and redistributes revenues among multiple operators — 17 different companies in the case of the Netherlands.

Built-in analytics

TransCity UP incorporates a native Big data analytics component that maximises the value of the data gathered for network operators. Sophisticated data clustering algorithms classify data points to detect correlations and anomalies. In Toronto[2], for example, this approach has identified some unexpected user profiles, such as "ghosts" (users who pay, but do not actually use public transport) and super-users (who make more than five journeys a day). These new insights are an opportunity for planners and operators to see the real picture rather than relying on assumptions, and to offer new services where they are needed and optimise their resources to meet the exact requirements of network users.

Another key benefit of the native Big data approach is the ability to analyse passenger flows in greater detail. Using visual analytics technologies, operators can build a more complete picture of passenger journeys that goes beyond conventional segmentations by day, time, zone and destination. For example, many systems only check tickets at the start of each journey; but recent experiments in Toronto have shown that the algorithms developed by Thales’s Big data specialists can automatically determine where passengers exit the system too.

Big data technologies give public transport operators and urban planners valuable insights into ridership patterns, powerful new ways to optimise their infrastructure and ensure their human resources and equipment are deployed in the right place at the right time. In Oslo, for example, this analytical approach has been used as a basis for planning new lines and devising new fare policies.

As public transport operators strive to increase cross-network connectivity and meet user demand for new payment options and fare media, Thales has integrated Big data technologies into its latest fare collection offering to enhance the traveller experience and boost operational efficiency at the same time. With Thales, the transportation solutions of the future are closer than they may seem!